Indoor Location Tracking Using Smartphones in a
Disaster Struck Scenario
Tamal Mondal
Indrajit Bhattachary
Amit Kumar Gupta
Kalyani Government Engineering College Kalyani Government Engineering College Kayani Government Engineering College
Kalyani,Nadia
Kalyani,Nadia
Kalyani,Nadia
[email protected]
[email protected]
[email protected]
Dibyadeep Mukherjee
Indranil Mukherjee
Government College of Engineering And Textile Technology
Serampore, Hoogly
[email protected]
Government College of Engineering And Textile Technology
Serampore, Hoogly
[email protected]
Abstract—In a disaster struck situation like earthquake, fire etc.
immediate evacuation might be necessary. In those scenarios
rescuing hostages or the way by which the trapped victims can
find their way out, is a big challenge, especially in those
circumstances where the communication link is broken or not
available. The proposed work aims in briefly describing several
procedures of tracking down the indoor location of any such
victim using Smartphone, depending upon the number of
available access point(s). In absence of any access point, that is in
a dark region victims may be tracked using a peer to peer selfconfigured Wi-Fi communication. Thus the application aims in
selecting a novel method of indoor localization, in any disaster
stuck situation, so that a victim might be tracked using available
infrastructures or devices.
Keywords— Access Point; GPS; Trilateration; Multilateration;
Peer to peer; RF Range; RSS; TOA; WFS
I.
INTRODUCTION
Development in Smartphone led users to utilize wide range of
services and functions available. Now-a-days most of the
Smartphone have build in GPS receivers, which is widely used
to track outdoor locations. According to a survey published by
American life project and Pew internet [1] more than 74%
adult Smartphone owners uses their Smartphone to find
directions or to get other information related to their present
location. Continuously tracking devices using GPS is very
energy consuming and it can drain the power of battery
operated devices in a very short time.
In a disastrous situation lasting of the battery power
is a big issue. Thus, it motivated us to develop an alternate
way for location tracking. In this paper we have proposed
three different solutions, depending upon the number of access
point available to the user. This approach might be used in
indoor locations as well, where satellite communications
cannot be established. These solutions have been integrated in
a single application that can be seamlessly switched to
appropriate cases.
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BACKGROUND
Mobile phone tracking [2] refers to the problem of
ascertaining of the position of a mobile phone stationary or on
the move. Localization might occur via Multilateration of
radio signals, between (several) radio towers of the network
and the phone, or simply by GPS. A GPS tracking unit [3] is a
device that uses the global positioning system (GPS) [4], in
order to determine the location of a person, vehicle or asset to
which it is attached. In addition to that it needs to record the
position of the assets, vehicles or people at a regular interval
of time. The battery behaviour of such system is noticeable
especially during the initial acquisition of navigation message
produced by the satellites: the state, ephemeris (satellite is not
able to transmit its position), and almanac. Acquiring each
satellite takes 12 to 30 seconds, but if the full almanac is
needed, this could take up to 12 minutes. During all of this,
the phone is unable to enter into a deep sleep mode. Therefore
continuously tracking devices using GPS, is very power
hungry and drains the power of the battery operated devices in
a very short time.
Global Navigation Satellite System (GPS or GNSS)
generally does not hold good in indoor places, since
attenuation and scattering might happen to the microwaves by
the roofs, walls or by other objects. Alternatively Wi-Fi based
positioning system (WFS) [5] can be used.
II.
SUMMARY OF WORK
The presented work has been summarized by briefly
explaining several scenarios to localize an indoor position [6]
depending upon the number of available access points. The
scenarios might be categorized in the following groups,
 Multiple i.e. more than two access points available.
 Less than or equal to two access points available.
 No access point is available (i.e. dark zones).
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trilateration techniques have been reported in the literature.
They are,
i) Wi-Fi trilateration based on signal propagation model and
ii) Wi-Fi trilateration based on RSS measurement collection.
Fig 1.1 Conditions, depending on which, three scenarios are identified .
Indoor location tracking is an important research issue now-adays. As GPS is not available in indoor scenario some dark
region may exist, hence an alternative to the GPS system need
to be developed that require minimum battery power of the
Smartphone and produces moderate amount of accuracy in
term of location information. In our case we have tried to
propose a novel Wi-Fi based indoor location tracking system.
In absence of any access point, the system is automatically
configured to a peer-to-peer self configured network. It can
also transmit emergency message to the control centre via
multihop communication.
INDOOR LOCALIZATION APPROACHES ON DIFFERENT
SCENARIOS:
Three major scenarios have been considered in case of
indoor localization. The scenarios can be grouped in
accordance with the number of access points available to the
person carrying Smartphone. The scenarios are described
below in more details:
The, signal strength parameter has been used to determine the
distance between the access points and the devices.
Considering three access points as AP1, AP2 and AP3 (as in
Fig 1.2), the distance between access points and the device has
been estimated, if signal strength of each access point is
known. These distances are the radius of the access points and
access points are centered by circle of respective radius. Let us
consider the estimated distances are e1,e2,e3 and the
coordinates of AP1, AP2 and AP3 are (a1,b1), (a2,b2), (a3,b3)
respectively .Then the equations of the circular areas will be,
e12 = (𝑥−a1)2+ (𝑦−b1)2
e22 = (𝑥−a2)2+ (𝑦−b2)2
e32 = (𝑥−a3)2+ (𝑦−b3)2
(1)
(2)
(3)
The intersection of three access points gives a point. But
practically, the intersections of the three or more access points
provide an area rather than a point. The trilateration algorithm
is basically used for indoor positioning and it might not give
accurate result sometimes.
It has been observed that the average positioning error
in this approach is 2 to 5 meters [8].
SCENARIO 1:
The first and foremost situation which has been considered, in
indoor localization is the case when a person (carrying
Smartphone) is within the range of three access points.
In this situation, the method of trilateration of Wi-Fi
positioning has been implemented to find out position of the
device, which aids a lot to move at a safe place in a disastrous
scenario. Whereas, in the same condition, if the availability of
access point is more than three, then multilateration technique
is used.
Fig: 1.2- Trilateration by Wi-Fi in indoor scenario
A. Wi-Fi trilateration
The trilateration [7] algorithm uses some parameters to
track the indoor location of a device. These parameters are,
 The frequency of the Wi-Fi signal
 The real co-ordinates of the access points nearer to
the device
 The signal strength.
This algorithm is used when there are three access points
nearby to a person carrying Smartphone. It is known that the
signal strength depends upon the distance between the
transmitter and the receiver. Signal strength decreases
exponentially if the distance between these two increases (due
to interference of noise and distortion). Two types of Wi-Fi
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Wi-Fi trilateration
Wi-Fi trilateration
based on signal
propagation model
Wi-Fi trilateration
based on RSS
measurement
collection
Fig: 1.3- Different approaches of Wi-Fi trilateration
The Wi-Fi trilateration method is used for indoor positioning
and provides less accurate localization. In order to improve it,
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we can use more accurate signal propagation models or
expanded measures of signal strength which includes most
number of reference points.
B. Wi-Fi Multilateration
When the numbers of access points are greater than or
equal to three, then Multilateration [9] localization might be
used. Here the absolute position of the target is based on
distance or range measured from four or more spatially
distributed sensors. Classical Multilateration technique
assumes that, the location of the anchor nodes are already
known and are already error free. Practically this assumption
may not hold good.
SCENARIO 2:
A person carrying Smartphone can also be tracked where the
number of access points is less than three.
Let us consider the case in which the number of access
point(s) is less than three but not zero i.e. one or two. In this
case, the distance might be calculated from the signal strength
of the available access point(s). As the received signal strength
varies in accordance with the distance from the access points,
thus calculating distance based on the signal strength might be
a good approach.
It is known that, as the wave front gets broadened the RF
signal weakens. It is the measurement of the free space path
loss i.e. the signal power loss in the device over a given
distance. It might be observed that the device losses nearly
0.020 dB per foot in outdoor or due to doors, walls, glasses
etc. That is why as the client walks away from the access
points the signal gets weakened.
Now the distance might be calculated by using this simple
formula:
(4)
This formula is derived from the free space path loss
phenomena [10]. Here the distance is measured in meters and
the frequency measured in MHz.
SCENARIO 3:
If there is no access point available and consequently no
network coverage present around a device, then peer to peer
(P2P) communications might be used. In P2P networks, there
are some mobile nodes, which can communicate with their
neighbouring nodes. The position of each node is computed
with the help of its neighbouring nodes. In this approach, even
when there is no network coverage, the location of the device
can be found.
Moreover if there is no access point available to the
victimized persons, the respective Smartphone is turned on in
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an ad hoc mode [11] to send messages like “I need help”, via
peer to peer communications. After receiving the messages the
nearby rescue workers available to them, could rescue them
safely. The P2P approach is advantageous in
following
manners,



Easy to set up.
Due to failure of one nearby node, the
communication would not hamper, as other nodes in
the neighbour might respond as well.
Less expensive approach.
In this case, the network performs a co-operative approach to
exchange data. Here each node sends emergency message via
its nearby neighbors to report its position. The main advantage
of this approach is that a victim can send an emergency
message even if there is no network infrastructure available.
Thus this Approach might be very much useful to track a
victim trapped at a dark region. The proposed approach is also
scalable to handle number of nodes. It encourages creating
time-varying heterogeneous networks with the help of
different receivers.
Wi-Fi DIRECT (P2P):
Wi-Fi Direct [12] is a new Wi-Fi standard that allows devices
to connect directly to each other at typical Wi-Fi speeds
without wireless access point. Due to high data transfer rate,
Wi-Fi Direct is becoming popular day by day. It is even
expected that it can replace the need of Bluetooth features on
future devices. It can also connect the devices which belong to
different manufacturers (like Samsung, Micromax etc.), which
can be considered as its biggest advantage.
Adding to the appeal of Wi-Fi Direct is the fact that, WiFi direct uses Wi-Fi Protected Setup (WPS) protocol and it is
also backward compatible. It does not require any extra
hardware on the device. It works as part of the standard Wi-Fi
Radio, allowing devices with old Wi-Fi setups to use the
functionality as well.
In a disastrous situation a victim might create a group or
might search for other groups that are already created. The
P2P group owner behaves like an access point and others are
termed as P2P clients. After creation of a group other clients
can join the group by a traditional Wi-Fi network. It is capable
of “one to many” type of connections establishment.
Therefore the victims can communicate their locations by
communicating via Wi-Fi direct with other nodes that might
be other victims who are not in a dark region, or with a rescue
worker. By this procedure the position of the node which is in
the dark region can be detected and reported.
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Fig 1.7 Measured and Actual distance vs Observations in point 3
Fig 1.4 Communicating via Wi-Fi Direct in P2P connections
III. EXPERIMENT AND RESULT
In our experimentation an Android Application has been
developed that can be used in Android platform froyo or
higher. In different scenarios we have collected data from the
applications to verify following parameters:


Accuracy of location tracking.
Battery consumption of the device.
Fig 1.8 Battery power consumption when the application is on
Fig 1.5, 1.6 and 1.7 depicts the measured distances and the
actual distances for different places around a particular area.
Fig 1.8 and 1.9 depicts the battery consumption when the
application is on and when GPS is turned on. In Fig 1.5 to1.7
the experimental results for three different places are shown.
For each place 20 observations were made and reported. In
Fig 1.8 and 1.9 two cases have been shown. In the first case
our application was turned on at 8.30 a.m. and closed on 10
a.m. and in the second case, the GPS was turned on at 8.30
a.m. and at 10 a.m. it was turned off. For both the cases the
battery power consumptions are shown below.
Fig 1.9 Battery power consumption when GPS is on
Fig 1.5 Measured distance and Actual distance vs Observations in point 1
Fig 1.6 Measured and Actual distance vs Observations in point 2
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The graphs show that the application is fairly accurate to
measure distances. Fig 1.5, 1.6 and 1.7 show that the accuracy
of the estimated distance by this application is about 84%,
94% and 99% respectively. While considering an indoor
scenario, an error of 8-10% can be easily neglected, as it
would not be a big problem to localize the area. From Fig 1.8
and 1.9 it can be observed that the application is energy
efficient too. As a Smartphone was used with turning on the
GPS system followed by turning on the application only, it
was found that the battery of the Smartphone lasted for 3 more
hours than in case of using only GPS. Due to the fact that
power saving is a big issue in a disastrous situation, it might
be better to use this application than the existing GPS system.
Another experiment was carried out to justify the
accuracy of measuring the location of a person in an indoor
scenario using the developed apps on the Smartphone.
It is known that as the distance from the access point
increases the signal gets weakened. Although during
experimentation, it was found that the signal levels could
fluctuate even when a person does not move with his device.
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Fig 2.1 and Fig 2.2 clearly depicts the amount of error in the
applications due to the fact described earlier. Nearly 8 meter
deviation was found in 20 repetitive observations. This error
has been generated due to diffraction and reflection caused by
nearby obstacles.
nearly impossible to get the actual signal strength. Therefore a
better approach might be, to use Time of Arrival (ToA) [12]
approach. The arrival time of a signal, transmitted from a
Smartphone can be measured precisely and as the velocity of
the signal is known, the distance between two devices can be
determined, by considering the elapsed in propagation time.
ACKNOWLEDGEMENT
This research work is an outcome of the Govt. of India Project
titled DiSARM funded by Information Technology Research
Academy, Media Lab. Asia, Dept. of E&IT.
REFERENCES
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Fig 2.1 error in the estimation for calculating the distance of access point
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Fig 2.2 error in the estimation for calculating the distance of access point in
point2
IV. CONCLUSION
In conclusions, the above results revealed that, the application
is much more battery efficient than the GPS location tracking
systems. The application was tested in a Smartphone with
2000 mAh battery. The application approximately lasted 3
hours more than that in the case of conventional GPS tracking
systems. Hence, it could be concluded that it is capable to save
at most 2000*3=6000mA battery power, which is fairly large
amount in a disastrous situations. Additionally, as the
application can operate on three different modes depending on
three different circumstances, it can be concluded that it would
be very much useful in a disastrous situation, where
evacuation of victims is the main challenge. Furthermore, the
results demonstrated that the application can accurately
determine the location of a victim from the nearby access
points in an accuracy of about 92%.
V. FUTURE SCOPE
In the application, the RSS signal strength of an access point
was used to determine the distance between the access point
and the Smartphone. However, lot of diffraction and reflection
might be caused by nearby obstacles. That is why it would be
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[6] CISCO, “Wi-Fi Location-Based Services 4.1 Design Guide, Location
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2013.
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